diff --git a/modules/mol/alg/pymod/lddt.py b/modules/mol/alg/pymod/lddt.py
index 1e1230f897281c125ea08fd60410f1b20c2367a7..40e98d90c6a287b0baf7eb687dd3a8c86c723ff2 100644
--- a/modules/mol/alg/pymod/lddt.py
+++ b/modules/mol/alg/pymod/lddt.py
@@ -254,6 +254,14 @@ class lDDTScorer:
self._sym_ref_distances_sc = None
self._n_distances_sc = None
+ # exactly the same as above but without intrachain contacts
+ # => inter-chain (ic)
+ self._ref_indices_ic = None
+ self._ref_distances_ic = None
+ self._sym_ref_indices_ic = None
+ self._sym_ref_distances_ic = None
+ self._n_distances_ic = None
+
# input parameter checking
self._ProcessSequenceSeparation()
@@ -317,11 +325,42 @@ class lDDTScorer:
self._n_distances_sc = sum([len(x) for x in self.ref_indices_sc])
return self._n_distances_sc
+ @property
+ def ref_indices_ic(self):
+ if self._ref_indices_ic is None:
+ self._SetupDistancesIC()
+ return self._ref_indices_ic
+
+ @property
+ def ref_distances_ic(self):
+ if self._ref_distances_ic is None:
+ self._SetupDistancesIC()
+ return self._ref_distances_ic
+
+ @property
+ def sym_ref_indices_ic(self):
+ if self._sym_ref_indices_ic is None:
+ self._SetupDistancesIC()
+ return self._sym_ref_indices_ic
+
+ @property
+ def sym_ref_distances_ic(self):
+ if self._sym_ref_distances_ic is None:
+ self._SetupDistancesIC()
+ return self._sym_ref_distances_ic
+
+ @property
+ def n_distances_ic(self):
+ if self._n_distances_ic is None:
+ self._n_distances_ic = sum([len(x) for x in self.ref_indices_ic])
+ return self._n_distances_ic
+
def lDDT(self, model, thresholds = [0.5, 1.0, 2.0, 4.0],
local_lddt_prop=None, local_contact_prop=None,
chain_mapping=None, no_interchain=False,
- penalize_extra_chains=False, residue_mapping=None,
- return_dist_test=False, check_resnames=True):
+ no_intrachain=False, penalize_extra_chains=False,
+ residue_mapping=None, return_dist_test=False,
+ check_resnames=True):
"""Computes lDDT of *model* - globally and per-residue
:param model: Model to be scored - models are preferably scored upon
@@ -350,6 +389,9 @@ class lDDTScorer:
:type chain_mapping: :class:`dict` with :class:`str` as keys/values
:param no_interchain: Whether to exclude interchain contacts
:type no_interchain: :class:`bool`
+ :param no_intrachain: Whether to exclude intrachain contacts (i.e. only
+ consider interface related contacts)
+ :type no_intrachain: :class:`bool`
:param penalize_extra_chains: Whether to include a fixed penalty for
additional chains in the model that are
not mapped to the target. ONLY AFFECTS
@@ -492,12 +534,22 @@ class lDDTScorer:
if len(sym_indices) > 0:
symmetries.append(sym_indices)
+ if no_interchain and no_intrachain:
+ raise RuntimeError("on_interchain and no_intrachain flags are "
+ "mutually exclusive")
+
if no_interchain:
sym_ref_indices = self.sym_ref_indices_sc
sym_ref_distances = self.sym_ref_distances_sc
ref_indices = self.ref_indices_sc
ref_distances = self.ref_distances_sc
n_distances = self.n_distances_sc
+ elif no_intrachain:
+ sym_ref_indices = self.sym_ref_indices_ic
+ sym_ref_distances = self.sym_ref_distances_ic
+ ref_indices = self.ref_indices_ic
+ ref_distances = self.ref_distances_ic
+ n_distances = self.n_distances_ic
else:
sym_ref_indices = self.sym_ref_indices
sym_ref_distances = self.sym_ref_distances
@@ -864,6 +916,38 @@ class lDDTScorer:
self._sym_ref_indices_sc,
self._sym_ref_distances_sc)
+ def _SetupDistancesIC(self):
+ """Select subset of contacts only covering inter-chain contacts
+ """
+ # init
+ self._ref_indices_ic = [[] for idx in range(self.n_atoms)]
+ self._ref_distances_ic = [[] for idx in range(self.n_atoms)]
+ self._sym_ref_indices_ic = [[] for idx in range(self.n_atoms)]
+ self._sym_ref_distances_ic = [[] for idx in range(self.n_atoms)]
+
+ # start from overall contacts
+ ref_indices = self.ref_indices
+ ref_distances = self.ref_distances
+ sym_ref_indices = self.sym_ref_indices
+ sym_ref_distances = self.sym_ref_distances
+
+ n_chains = len(self.chain_start_indices)
+ for ch_idx, ch in enumerate(self.target.chains):
+ chain_s = self.chain_start_indices[ch_idx]
+ chain_e = self.n_atoms
+ if ch_idx + 1 < n_chains:
+ chain_e = self.chain_start_indices[ch_idx+1]
+ for i in range(chain_s, chain_e):
+ if len(ref_indices[i]) > 0:
+ inter_idx = np.where(np.logical_or(ref_indices[i]<chain_s,
+ ref_indices[i]>=chain_e))[0]
+ self._ref_indices_ic[i] = ref_indices[i][inter_idx]
+ self._ref_distances_ic[i] = ref_distances[i][inter_idx]
+
+ self._NonSymDistances(self._ref_indices_ic, self._ref_distances_ic,
+ self._sym_ref_indices_ic,
+ self._sym_ref_distances_ic)
+
def _CloseStuff(self, pos, inclusion_radius, indices, mask_start, mask_end):
"""returns close stuff for positions specified by indices
"""
diff --git a/modules/mol/alg/tests/test_lddt.py b/modules/mol/alg/tests/test_lddt.py
index 9f2a079346c48f92dcd94c7cbb41f09d999317f4..6f3e3be47386b1a50cc42d4330fdb052869daa02 100644
--- a/modules/mol/alg/tests/test_lddt.py
+++ b/modules/mol/alg/tests/test_lddt.py
@@ -184,6 +184,40 @@ class TestlDDT(unittest.TestCase):
scorer.lDDT(model, check_resnames=False)
+ def test_intra_interchain(self):
+ ent_full = _LoadFile("4br6.1.pdb")
+ model = ent_full.Select('peptide=true and cname=A,B')
+ target = ent_full.Select('peptide=true and cname=A,B')
+ chain_mapping = {"A": "A", "B": "B"}
+
+ lddt_scorer = lDDTScorer(target)
+
+ # do lDDT only on interchain contacts (ic)
+ lDDT_ic, per_res_lDDT_ic, lDDT_tot_ic, lDDT_cons_ic, \
+ res_indices_ic, per_res_exp_ic, per_res_conserved_ic =\
+ lddt_scorer.lDDT(model, no_intrachain=True,
+ chain_mapping = chain_mapping,
+ return_dist_test = True)
+
+ # do lDDT only on intrachain contacts (sc for single chain)
+ lDDT_sc, per_res_lDDT_sc, lDDT_tot_sc, lDDT_cons_sc, \
+ res_indices_sc, per_res_exp_sc, per_res_conserved_sc =\
+ lddt_scorer.lDDT(model, no_interchain=True,
+ chain_mapping = chain_mapping,
+ return_dist_test = True)
+
+ # do lDDT on everything
+ lDDT, per_res_lDDT, lDDT_tot, lDDT_cons, res_indices, per_res_exp, \
+ per_res_conserved = lddt_scorer.lDDT(model,
+ chain_mapping = chain_mapping,
+ return_dist_test = True)
+
+ # sum of lDDT_tot_ic and lDDT_tot_sc should be equal to lDDT_tot
+ self.assertEqual(lDDT_tot_ic + lDDT_tot_sc, lDDT_tot)
+
+ # same for the conserved contacts
+ self.assertEqual(lDDT_cons_ic + lDDT_cons_sc, lDDT_cons)
+
class TestlDDTBS(unittest.TestCase):